Genotyping-by-sequencing for plant breeding and genetics 1 2 ...

The Plant Genome: Published ahead of print 10 Sept. 2012; doi: 10.3835/plantgenome2012.05.0005

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Genotyping-by-sequencing for plant breeding and genetics

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Jesse A. Poland* and Trevor W. Rife

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J.A. Poland, United States Department of Agriculture – Agricultural Research Service,

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University, 4008 Throckmorton Hall, Manhattan KS, 66506; T.W. Rife,

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Hard Winter Wheat Genetics Research Unit and Department of Agronomy, Kansas State

Interdepartmental Genetics, Kansas State University, 4024 Throckmorton Hall, Manhattan KS, 66506

* Corresponding author: [email protected]

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Abbreviations:

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sequencing

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GBS, genotyping-by-sequencing; GS, genomic selection; NGS, next-generation


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Abstract

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$1000 human genome within reach while providing the raw sequencing output for

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advancements, genotyping-by-sequencing (GBS) has been developed as a rapid and

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combines genome-wide molecular marker discovery and genotyping. The flexibility and

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Rapid advances in “next-generation” DNA sequencing technology have brought the

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researchers to revolutionize the way populations are genotyped. To capitalize on these

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robust approach for reduced-representation sequencing of multiplexed samples that

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low-cost of GBS makes this an excellent tool for many applications and research

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opportunities that are becoming more feasible with GBS. Further, we highlight areas

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output, development of reference genomes, and improved bioinformatics. The ultimate

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genotype can then be used to predict phenotypes and select improved cultivars.

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resulting phenotypes will enable genomics-assisted breeding to exist on the scale needed

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questions in plant genetics and breeding. Here we address some of the new research

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where GBS will become more powerful with the continued increase of sequencing

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goal of plant biology scientists is to connect phenotype to genotype. In plant breeding the

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Furthering our understanding of the connection between heritable genetic factors and the

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to increase global food supplies in the face of decreasing arable land and climate change.

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“Next-generation” genotyping

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generation sequencing (NGS) output have provided technology with the ability to greatly

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Driven by the quest for a $1000 human genome, rapid advances in next-

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transform the way we think about plant genomics and breeding. With the introduction of

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months (Figure 1). The availability of inexpensive sequencing technology has

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polymorphisms are discovered (Mardis, 2008; Futschik and Schlötterer, 2010; You et al.,

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al., 2012), and populations are genotyped (Baird et al., 2008; Elshire et al., 2011; Davey

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rapidly becoming so inexpensive that it will soon be reasonable to use it for every genetic

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and the practice of applied plant breeding.

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connect phenotype to genotype and use this knowledge to make phenotypic predictions

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populations with dense molecular markers across the genome. To put the power of NGS

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have been developed. One promising approach is genotyping-by-sequencing (GBS)

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only a small portion of the genome) coupled with DNA barcoded adapters to produce

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demonstrated to be robust across a range of species and capable of producing tens to

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The flexibility of GBS in regards to species as well as populations and research

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massively parallel sequencing, raw sequencing output is doubling roughly every 6

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transformed the way genomes are sequenced (Xu et al., 2011; Wang et al., 2011),

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2011; Nielsen et al., 2011), gene expression is analyzed (Geraldes et al., 2011; Harper et

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et al., 2011; Truong et al., 2012; Poland et al., 2012; Wang et al., 2012). Sequencing is

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study. NGS applications have the potential to revolutionize the field of plant genomics

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One of the primary objectives of functional genomics in agricultural species is to

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and select improved plant types. To do this on a genome-wide scale requires large

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to work for plant breeding and genomics, new approaches for sequence-based genotyping

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which uses enzyme-based complexity reduction (using restriction endonucleases to target

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multiplex libraries of samples ready for NGS sequencing. This approach has been

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hundreds of thousands of molecular markers (Elshire et al., 2011; Poland et al., 2012).

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objectives makes this an ideal tool for plant genetics studies. As the phenomenal increase

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in NGS output continues, many research questions that were once out of reach will be

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resolved through the application of these approaches.

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All in one

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The two key components for genotyping germplasm are finding DNA sequence

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polymorphisms and assaying the markers across a full set of material. Classically, this

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genotyping. An important strength of sequence-based genotyping approaches is that the

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exploration of new germplasm sets or even new species without the upfront effort of

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is that the raw data is dynamic. The raw sequences obtained from GBS can be re-

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has been a two-step process involving marker discovery followed by assay design and

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marker discovery and genotyping are completed at the same time. This facilitates

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discovering and characterizing polymorphisms. Another key component of GBS datasets

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analyzed, uncovering further information (e.g. new polymorphisms, annotated genes,

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collection of sequence data increases. Each of these factors adds additional value to the

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etc.) as bioinformatics techniques improve, reference genomes develop, and the

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same raw dataset.

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nucleotide polymorphism (SNP) and presence/absence variation (PAV) discovery in

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al., 2008; Gore et al., 2009a; b; Huang et al., 2009; Deschamps et al., 2010; Hyten et al.,

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These studies have focused on assaying a few key genotypes with a reduced-

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et al., 2009). While highly effective for SNP discovery, this approach is limited in the

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population of interest.

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polymorphisms and then transfer these to a fixed assay, but to simultaneously discover

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It is this combined one-step approach that makes GBS a truly rapid and flexible platform

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One of the first and broadly adapted applications for utilizing NGS was for single

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diverse populations with and without reference genomes (Baird et al., 2008; Wiedmann et

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2010; You et al., 2011; Nelson et al., 2011; Hohenlohe et al., 2011; Byers et al., 2012).

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representation approach (Baird et al., 2008) or with whole-genome re-sequencing (Huang

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number of lines assayed and does not simultaneously assay the markers across the full

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The key objective of the GBS approach, therefore, is not merely to discover

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polymorphisms and obtain genotypic information across the whole population of interest.

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for a range of species and germplasm sets and perfectly suited for genomic selection in

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plant breeding programs. As sequencing output continues to increase, GBS will evolve

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to whole-genome re-sequencing (to capture all variants). Whole genome re-sequencing

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first to lower levels of complexity reduction (to capture more sequence variants) and then

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has been applied in Arabidopsis, rice, and maize (Huang et al., 2009; Ashelford et al.,


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2011; Gan et al., 2011; Chia et al., 2012; Jiao et al., 2012; Xu et al., 2012), though it

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reference genome (Morrell et al., 2011). The level of multiplexing has also been limited

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As GBS can be readily used for de novo discovery and application of new

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quickly becomes less manageable with larger, more complex genomes that lack a solid

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in this approach, increasing per sample cost.

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molecular polymorphisms, it is particularly powerful for new sets of germplasm and

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genotyping approaches is reducing ascertainment bias associated with marker discovery

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uncharacterized species. In many ways the greatest advantage of sequence-based

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in panels differing from the target population. This is an obvious advantage for

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precision of the study (Myles et al., 2009; Hamblin et al., 2010). For breeding

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introduced into the breeding pool. The use of an unrepresentative marker panel in

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molecular diversity present in a target population. Most GBS approaches utilize

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of the genome, ascertainment bias could potentially be introduced in different sets of

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should have little bias across sets of germplasm, it is also unknown how uniformly they

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that GBS markers were uniformly spaced across the chromosomes of both wheat and

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association studies where differing allele frequencies greatly influence the power and

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applications, informative polymorphisms can be discovered as novel germplasm is

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surveying molecular diversity is highly problematic for getting a true representation of

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methylation-sensitive enzymes. If these enzymes target differentially methylated regions

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germplasm, but evidence for this has yet to be seen. While markers discovered with GBS

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are spaced across the genome. Evidence from Poland et al. (2012), however, indicated

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barley.

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Many flavors

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genome was first demonstrated by Altshuler et al. (2000). This approach was then later

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The use of reduced-representation sequencing for targeting small portions of the

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combined with NGS and DNA barcoded adapters to sequence multiplex libraries in

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“genotyping using “next generation sequencing of multiplex DNA-barcoded reduced-

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parallel. There are many variations of this approach and GBS is one specific method for

representation libraries” (Table 1). Further, the combination of enzymes that can be employed for complexity reduction is almost endless. Davey et al. (2011) has thoroughly


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reviewed several approaches of complexity reduction including complexity reduction of

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reduced representation libraries (van Tassell et al., 2008).

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combined with NGS was first described by Baird et al. (2007) and termed Restriction

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fragments which are then ligated to an adapter containing a forward primer for

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sample multiplexing (Baird et al., 2008; Craig et al., 2008; Cronn et al., 2008). The

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similarly-sized DNA fragments (Baird et al., 2008). The fragments are then ligated to a Y

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et al., 2008). RAD markers provided a robust method to discover polymorphisms and

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polymorphic sequences (CRoPS™) (van Orsouw et al., 2007) and deep sequencing of

The use of restriction enzymes for targeted reduction of genome complexity

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Association DNA (RAD). RAD methods use a restriction enzyme to generate genomic

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amplification, sequencing platform primer sites, and a unique DNA barcode that enables

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samples are pooled, randomly sheared, and size-selected to create a uniform collection of

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adapter that ensures only fragments containing the first adapter will be amplified (Baird

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map variation in a population (Miller et al., 2007).

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based marker technologies: the requirement of species-specific arrays, a hybridization for

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Combining the progressive features of RAD with NGS, however, resulted in the

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First generation RAD analysis had similar drawbacks to older restriction enzyme-

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every comparison, and a limitation to presence variation assays (Baird et al., 2008).

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discovery of new markers at a significantly decreased cost (Baird et al., 2008). The

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mapping of many polymorphisms and precise assignment of chromosomal regions to

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has recently been modified to utilize restriction enzymes that cut upstream and

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length tags, allows nearly all of the restriction sites to be surveyed, and permits marker

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simultaneous discovery of SNP markers during RAD sequencing facilitated robust

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mapping parents, allowing for detection of recombination locations. The RAD approach

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downstream of a target site (Wang et al., 2012). This new methodology produces uniform

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intensity adjustment (Wang et al., 2012). The next flavor of sequence-based genotyping

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eliminated DNA shearing, required less starting DNA, and implemented a Hidden

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was multiplexed shotgun genotyping (MSG) which required only one gel purification,

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Markov Model (HMM) to determine points of chromosomal recombination (Andolfatto

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limited complexity reduction suitable for the smaller genome (~130Mb) of Drosophila

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et al., 2011). MSG employed a single common cutting restriction enzyme and produced a


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simulans (Andolfatto et al., 2011). In the context of a good reference genome, the HMM

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recombination break points (Andolfatto et al., 2011).

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construction of RAD libraries (Elshire et al., 2011). The strength of the GBS protocol is

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avoiding shearing and size selection (Figure 2). . The GBS approach removed the need

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the Y-adapters used in the RAD protocol, the original GBS protocol utilized a single

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Although all combinations of adapters can ligate to the DNA fragments, only those that

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2011).

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combines a rare- and a common-cutting restriction enzyme to generate uniform libraries

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each fragment (Poland et al., 2012). The use of two enzymes in this GBS approach

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employment of a Y-adapter on the common restriction site avoids amplification of more

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approach has been successfully applied in several species including cotton (Gossypium

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little to no change in protocol (Jesse Poland, unpublished).

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imputation approach was highly effective for tracing parental origin and defining

The original GBS protocol was developed to simplify and streamline the

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its simplicity: utilizing inexpensive adapters, allowing pooled library construction, and

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for size selection by using a short PCR extension of the multiplexed library. Instead of

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restriction enzyme, a barcoded adapter, and a common adapter (Elshire et al., 2011).

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contained one of each barcode are able to be amplified and sequenced (Davey et al.,

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The original GBS approach was recently extended to a two enzyme version that

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consisting of a forward (barcoded) adapter and a reverse (Y) adapter on alternate ends of

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enables the capture of most fragments associated with the rare-cutting enzyme. The

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common fragments, a preferential situation for larger, more complex genomes. The GBS hirsutum), oats (Avena sativa), sorghum (Sorghum bicolor) and rice (Oryza sativa) with The options for tailoring GBS to any species or desired application are almost

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endless. A range of enzymes including ApekI, PstI and HindIII have been evaluated in

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personal communication). With a varied level of complexity reduction, it is possible to

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population. The interplay of these two factors will determine the optimal approach for the

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use of rare-cutting restriction enzymes (i.e. 6 bp or greater target site) with methylation

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maize with success in varying the level of complexity reduction (Edward Buckler,

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increase coverage of a target genome or increase the multiplexing level of a target

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species under investigation. For species with large genomes or no reference genome, the


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sensitivity can assist in creating a higher level of complexity reduction by targeting fewer

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amount of missing data.

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Hand-in-hand with the reference genome

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(sequenced) reference genome. A reference genome makes ordering and imputing low

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approaches straightforward. This has been seen in many of the reported uses of sequence-

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the D. simulans reference genome to first align tags to the reference and then call SNPs.

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segregating in the population. This approach is very robust for assigning parent-of-origin

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rice to first align NGS tags and subsequently call SNPs. The physical ordering of these

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for segregating populations.

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genome, the rapid discovery and ordering (through genetic mapping) of sequence-based

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genome. High-density genetic maps developed through GBS can be used to anchor and

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Andolfatto, et al. (2011) were able to assign 8 Mb to linkage groups, which comprised

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substantial improvement of an already well-characterized genome. Likewise, in current

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Gb) (Arumuganathan and Earle, 1991), high-density GBS maps are being used to assist

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contigs (N. Stein et al., in press). This approach appears very promising, creating a

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sites. This will lead to higher sampling depth of the same genomic sites and reduce the

Sequence-based genotyping greatly benefits from a well-characterized

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coverage marker data generated through GBS and other sequence-based genotyping

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based genotyping. The MSG approach employed by Andolfatto et al. (2011) made use of

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Using a physical map framework, the parent-of-origin was then imputed across all SNPs

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in bi-parental populations. Likewise, Huang et al. (2009) used the reference genome of

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markers greatly enabled and simplified the imputation and assignment of parent-of-origin

Though genotyping-by-sequencing approaches greatly benefit from a reference

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molecular markers can greatly assist with the development and refinement of a reference

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order physical maps and refine or correct unordered sequence contigs. In D. simulans,

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30% of the unassembled D. simulans genome or about 6% of the total genome. This is a

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efforts in much larger, more complex genomes including barley (5.5 Gb) and wheat (16

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with anchoring and ordering large numbers of assembled but unanchored and unordered

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positive feed-back loop where the development of the reference genome assisted by GBS

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markers leads to better SNP calling and order-based imputation for GBS datasets.


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Maps made easy

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development of genetic maps for characterizing segregating populations exceptionally

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genetic map can serve the same purpose. For characterizing a new population, there will

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frequencies, or order markers. With a reference genome, markers can be ordered along

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recombination break points. The power of such approaches has been highlighted in recent

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al., 2010), and maize (Elshire et al., 2011). Even at low coverage, the placement of sparse

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intervals (Huang et al., 2009; Xie et al., 2010). This approach can be extended to

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Andolfatto et al. (2011) demonstrated a hidden Markov Model that accurately inferred

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have successfully been applied in maize, as well (P. Bradbury, personal communication).

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be accomplished through development of a reference genetic map for the species of

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density genetic map (Poland et al., 2012). For new populations, GBS tags can be used to

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map. The extremely large number of markers produced with GBS allows sufficient

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The combination of GBS with a well-defined reference genome makes the

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straightforward. In the absence of a solid reference genome, a high-density reference

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no longer be any need to place markers on linkage groups, calculate recombination

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the physical chromosome (Figure 3). This ordering can then be used to precisely place

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papers with model species including D. simulans (Andolfatto et al., 2011), rice (Huang et

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markers on the physical map can be used to narrow points of recombination to 100-200kb

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populations with heterozygous chromosomal segments such as F 2 or BC 1 populations.

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heterozygous states from low-pass sequence-based genotyping. These same approaches

In the absence of a solid reference genome, the same ease of genetic mapping can

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interest. GBS markers and other framework markers can be integrated to develop a high-

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make genotype calls based on the reference map without the need to construct a de novo

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coverage for most populations even if only a fraction of the total markers are utilized.

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be broadly applied to the characterization of populations of interest for breeding and

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selection, near-isogenic lines, and alien-introgression lines. The use of a variety of

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will add value to inferences and conclusions for molecular breeding and selection

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These same approaches for developing genetic maps and graphical genotypes can

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germplasm improvement including elite breeding lines, segregating populations for

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algorithms to correctly infer the heterozygous/homozygous state of chromosome regions


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(Andolfatto et al., 2011). Other algorithms can be used for phasing markers in

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marker order of the GBS SNPs.

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Mapping single genes

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mapping single genes. The de novo discovery of high-density markers in a population of

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segregating and outcrossing populations. This will generally, however, require known

GBS and other sequence-based genotyping approaches can be very powerful for

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interest has the potential to circumvent the cumbersome process of marker discovery and

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RAD markers have been used in bulked segregant analysis to quickly identify linked

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to rapidly identify segregating polymorphisms. In lupin (Lupinus angustifolius), Yang et

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testing for fine-mapping of target genes and mutations. In the absence of a reference map,

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markers (Baird et al., 2008). For single genes of interest, this can be a valuable approach

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al. were able to identify 30 markers linked to an Anthracnose resistance gene (Yang et

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is that the per-sample cost will be low enough that individual samples can be used rather

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al., 2012). One advantage of GBS for mapping single genes in F 2 or similar populations

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than bulks. This will allow correction or removal of any individuals that were incorrectly

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application, there will be a balance between finding markers linked to the gene of interest

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breeding approaches, it can still be optimal to pre-screen populations with markers for

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phenotyped while confirming segregation of linked markers. Depending on the

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using GBS and developing single marker assays from the resulting data. Considering

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known single genes (with large effects) for smaller investment in time and sample costs

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then be genotyped using GBS for genomic selection.

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An Excess of Markers

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genes is a viable breeding strategy, sequencing capacity is becoming so inexpensive and

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germplasm of interest. Previously, scientists spent a majority of their time developing and

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number of markers to complete. GBS, however, can readily generate tens of thousands of

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prior to conducting whole genome profiling. Selected plants carrying desired genes can

While pre-selection of breeding populations for single markers for important

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readily available that it will soon be reasonable to generate whole-genome profiles on any

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working with a small number of markers. Many projects today still only require a small


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usable markers which can be selectively filtered into the few required for a target

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possible, genomic selection models have diminishing returns on additional markers once

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et al., 2011). On the other hand, for association mapping studies, additional markers

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2010). The current limitation for the generated data is computational. There are new

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resources needed to make these quantitative genetics questions more manageable

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to manage breeding data and develop models. At the same time, bioinformatics training

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experiment. While statistical geneticists will always prefer to have as many markers as

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the population has reached the point of “marker saturation” (Jannink et al., 2010; Heffner

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increase the likelihood of finding and tagging causal polymorphisms (Cockram et al.,

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algorithms and developments in cluster computing to provide the computational

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(Stanzione, 2011). Quantitative geneticists and bioinformatics personnel will be needed

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will become a more central component to any plant breeding and genetics curriculum.

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Filling in the blanks

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often have a significant amount of missing data due to low coverage sequencing (Davey

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The “catch” to GBS and sequence-based genotyping in general, is that datasets

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et al., 2011). Biologically, missing genotyping calls in GBS datasets can be the result of

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methylation. On the other hand, the technical issue of missing data with GBS is a

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sequence coverage of the library.

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and the choice of enzyme(s) used for complexity reduction. Enzymes with a shorter

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recognition site. Methylation-sensitive enzymes will greatly reduce the number of

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generate around 500,000 - 600,000 unique tags while in wheat around 1.5M tags are

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dataset is substantially higher partly due allelic variants, but largely due to sequencing

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“unique” tags.

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presence-absence variation, polymorphism in restriction sites, and/or differential

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combination of 1) library complexity (i.e., number of unique sequence tags) and 2)

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Library complexity is directly related to the species’ genome under investigation

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recognition site will naturally produce more fragments than those with a longer

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fragments in species with large portions of repetitive DNA. In barley, PstI-MspI libraries

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generated (J. Poland, unpublished). The actual number of sequence tags present in a raw

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errors, many of which can be non-random. This can and will generate many versions of


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The level of missing data is based on the sequencing coverage. The sequencing

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coverage is a function of the library complexity, multiplexing level, and the output of the

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independent sequences generated from the sequencing platform will determine the

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sample, while increased sequencing output (when using the same multiplexing level) will

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sequencing platforms is the number of independent reads. Post-Sanger sequencing

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sequencing platform (Andolfatto et al., 2011). The multiplexing level and the number of

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average number of reads per sample. Higher multiplexing levels will reduce the data per

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understandably increase the data per sample. One key component of GBS on different

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platforms generally rely on a large number of short sequence reads to produce gigabases

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of sequence data (Metzker, 2009). The new platforms are continually increasing the

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longer reads is less advantageous than generating more reads. More sequence reads

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multiplexing levels with static amounts of data per sample. For GBS, 10 Gb of sequence

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While increasing the number of reads is clearly advantageous for GBS, longer reads are

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with limited diversity) and assisting GBS applications in polyploids where secondary,

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homeologous sequences on other genomes.

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The logical approach to removing missing data is to sequence to a higher depth by

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sequencing output, a function of more and longer reads. For GBS, however, generating

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provides more data per sample. Alternatively, increasing read numbers allows higher

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data generated from 100M reads of 100 bp would be preferable to 10M reads of 1,000 bp.

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also beneficial, leading to the discovery of more polymorphisms (particularly in species

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genome-specific polymorphisms are needed to differentiate a segregating SNP from

Missing data can be dealt with by 1) sequencing to higher depth or 2) imputing.

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reducing the multiplexing level or sequencing the library multiple times. This can be very

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association mapping panels or parents of a breeding program, however, the additional

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applications using GBS with targeted selection, other approaches to minimize the impact

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minimizing genotyping cost will take preference over minimizing missing data.

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the type of GBS libraries, and the overall size of the datasets, imputation can give very

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effective (Figure 4) but has the drawback of increaseing per sample cost. For important

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investment to generate higher coverage of the tags is likely worthwhile. For breeding

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of missing data are preferable. Since a majority of the population will be discarded,

The second approach is imputation of missing data. Depending on the genome,


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accurate results. There are many imputation algorithms (Marchini et al., 2007; Purcell et al., 2007; Browning and Browning, 2007), most of which are targeted toward haplotype

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reconstruction on a reference genome. Other approaches such as a Random Forest model

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Sequencing diverse, key individuals in the population (parents or representatives of

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(Breiman, 2001) can be used to impute unordered markers (as is the situation in wheat).

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kinship clusters) can greatly improve imputation accuracy by defining known haplotypes

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Finally, a matrix of realized relationships among individuals in a breeding

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for the population.

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population can be constructed without imputation. For very high-density genotyped data

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disequilibrium present in most breeding programs. From this perspective, it is only

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present in both individuals. With high marker density, there will still be tens of thousands

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most elite breeding material. Imputation with the simple marker mean can still produce

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generated by GBS, the marker coverage is sufficient to saturate the genomic linkage

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necessary to determine a pair-wise identity between individuals for the markers that are

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of pair-wise comparisons between two individuals, well beyond the saturation point for

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accurate genomic selection prediction models. From a genomic selection perspective,

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kinship-based marker imputation can be used to optimize the realized relationship matrix

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concurrent submission). This approach has been shown to improve the relationship

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in the presence of a high-level of missing data (Poland et al., The Plant Genome,

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estimates and give more accurate genomic selection model predictions.

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Association mapping

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association mapping (AM). One key to applying GBS for AM mapping is addressing the

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GBS has the potential to be an excellent tool for genotyping of diverse panels for

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missing data problem. As noted previously, higher coverage sequencing will reduce the

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AM panel that will be well characterized, extensively phenotyped, and serve as a

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achieve high coverage is likely worth the investment. This will produce a very well-

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become a very precise exercise, particularly on populations with extensive linkage

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amount of missing data at the expense of increased per sample costs. For a high-value

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community resource population, the additional cost of sequencing several times to

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characterized genetic population. At a high coverage, imputation of missing data will


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disequilibrium. Depending on the species under interrogation, the GBS markers will need

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In such populations, GBS markers also have the advantage of being able to survey

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to be ordered via a physical reference map or through genetic mapping.

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multiple haplotypes on a fine scale. When two or more SNPs are within the same tag,

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uncover these alleles. Array-based methods, particularly those applied to polyploid

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duplicated sequence will indicate an allele call (for the ancestral allele) even if the target

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discrimination between duplicated sequences. At higher sequencing coverage of the GBS

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pool of sequenced tags.

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Genomic Selection

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is to create a low-cost genotyping platform capable of generating high-density genotypes.

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these SNP alleles are both evaluated concurrently. For PAVs, GBS also has the power to

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species, are limited in the ability to accurately survey PAVs as hybridization to a

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locus is absent. Due to the context sequence accompanying a SNP, GBS enables

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library, PAV can then be inferred by the absence of a given tag for a given sample in the

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In the field of plant breeding, an important objective in the development of GBS

For genomic selection in crop species, breeders need a fast, inexpensive, flexible method

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that will enable genotyping of large populations of selection candidates. A majority of the

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low-cost genotyping. GBS is quickly expanding to fill those requirements.

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approach to capture the full complement of small effect loci in genomic prediction

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fitting effects to all markers and avoiding statistical testing. By utilizing these GS models,

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selection candidates are then discarded, creating a situation that is greatly benefited from

Genomic selection (GS) was proposed in 2001 by Meuwissen, et al. as an

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models. GS takes advantage of dense genome-wide molecular markers by simultaneously

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breeders are able to predict the performance of new experimental lines at early

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(Jannink et al., 2010). Combined with a fast turn-around on generations, selection based

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increase gains in plant breeding programs (Meuwissen et al., 2001; Jannink et al., 2010).

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needed for generating tens to hundreds of thousands of molecular markers. Poland et al,

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generations and generate suggested crosses and selections based on the model predictions

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on predicted breeding values determined by marker data provided by GBS could greatly

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The advantage of GBS for GS in breeding programs is the low per sample cost


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(2012, The Plant Genome, concurrent submission) have demonstrated the suitability for

415

demonstrate prediction accuracies for yield and other agronomic traits that are high

417

significant improvement in the attained prediction accuracy over a previously used array

419

implications in breeding. The training population was genotyped without a priori

414

GBS markers in developing GS models in the complex wheat genome. They were able to

416

enough to be suitable for breeding applications. The GBS markers also showed a

418

of hybridization-based markers. The important finding of this work is the practical

420

knowledge of the population or SNPs and per sample cost was below $20.

422

Putting GBS to work

424

every genomic problem. These marker datasets are low-cost and dynamic, with data and

421

423

Looking forward, high-density markers from NGS will soon be applied to almost

425

genotyping results getting more robust and economical each year. GBS has been shown

427

al., 2012), breeding applications (Poland et al., concurrent submission), and diversity

426

to be a valid tool for genetic mapping (Baird et al., 2008; Elshire et al., 2011; Poland et

428

studies (Fu, 2012; Lu et al., 2012). The ability to quickly generate robust datasets without

430

plagued researchers working with obscure or foreign species: a lack of defined and

432

platform for studies ranging from quickly identifying single gene markers to whole

429

considerable prior effort for marker discovery is quickly dispelling issues that have

431

specific genetic tools for genome analysis (Allendorf et al., 2010). GBS is an ideal

433

genome profiling of association panels.

435

breeding. Theoretical and preliminary studies on genomic selection show great promise

437

and low-cost tool for genotyping these populations, allowing breeders to implement GS

439

will drive per sample cost below $10. Further, there is no requirement for a priori

441

range of species and SNP discovery and genotyping are completed together. This is a

443

understudied genomes and commercial crops with large and complex genomes.

434

Perhaps one of the most exciting applications of GBS will be in the field of plant

436

for accelerating the rate of developing new improved varieties. GBS is providing a rapid

438

on a large scale in their breeding programs. Current developments in sequencing output

440

knowledge of the species as the GBS methods have been shown to be robust across a

442

very important feature for moving genomics-assisted breeding into orphan crops with


444

Challenges remaining include data management as well as modeling genotype-by-

446

stands to be a major supplement to traditional crop development. The potential for GBS

445

environment interactions, though the future looks promising. Genomic selection via GBS

447

data to improve breeding systems through GS is enormous.

449

genomic studies will have an important place well into the future. Driven by applications

451

developments in next-generation sequencing and genomics platforms must be put to use

448

Application of sequence-based genotyping for a whole range of diversity and

450

across the whole spectrum of human, microbial, plant, and animal genomics,

452

for plant breeding and genetics studies.

453 454

455

ACKNOWLEDGMENTS

457

Project (T-CAP) (2011-68002-30029) provided support for T. Rife. This manuscript was

459

trade names or commercial products in this publication is solely for the purpose of

461

the U.S. Department of Agriculture. USDA is an equal opportunity provider and

456

USDA-ARS and the USDA-NIFA funded Triticeae Coordinated Agriculture

458

greatly improved by the helpful comments of two anonymous reviewers. Mention of

460

providing specific information and does not imply recommendation or endorsement by

462

employer.

463 464


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643

644

FIGURES Figure 1 – A comparison of actual sequencing capacity (orange) to what would be

645

expected if sequencing technology was following Moore’s Law (blue). The

647

generation sequencing technology. Data from National Human Genome Research

646

significant decrease in 2007 coincides roughly with the introduction of next-

648

Institute (http://www.genome.gov/sequencingcosts/).

649

650

Figure 2 – Schematic overview of steps in GBS library construction, sequencing and

651

analysis. 1) Genomic DNA is quantified using florescence-based method. 2) gDNA

653

of all samples and equal molarity of gDNA and adapters. 3) A master mix with

655

barcoded adapters are added along with ligase and ligation bufferes. 5) Samples are

657

is cleaned and evaluated on a capillary sizing system. 8) Libraries are ready to

659

Data analysis: Following a sequencing run, Fastq files containing raw data from the

661

Once assigned to individual samples, the reads are aligned to a reference genome.

663

internally aligned (alignment of all sequence reads will all other reads from that

665

algorithms can then be used to distinguish true bi-allelic SNPs from sequencing

652

is normalized in a new plate. Normalization is needed to ensure equal representation

654

restriction enzyme(s) and buffer is added to the plate and incubated. 4) DNA

656

pooled and cleaned. 6) The GBS library is PCR amplified. 7) The amplified library

658

sequence.

660

run are used to parse sequencing reads to samples using the DNA barcode sequence.

662

In the case of species without a complete reference genomic sequence, reads are

664

library) and SNPs identified from 1 or 2 bp sequence miss-match. Various filtering

666 667 668 669 670 671

672

673

errors.

Figure 3 – Integration of genotyping-by-sequencing in the context of plant breeding and genomics for a species without a completed reference genome.

Figure 4 – Removal of missing data in genotyping-by-sequencing by increasing coverage of the library via re-sequencing. In a set of international wheat breeding germplasm, several lines (samples) were replicated across two or more libraries. Replicating a


674

sample two times increased the coverage of SNPs to 60%, while five replications

676

missing data replicated sequencing increases the per sample cost. The average per

678

equivalent to the sequencing coverage of the library (i.e. 5 replications ~ 5X

675

increase the coverage to over 90%. While very effective as a means to remove

677

sample cost is $15. In this situation for wheat, the number of replications is roughly

679

coverage). Data from Poland, et al. (unpublished).

680 681 682 683 684 685 686 687

688 689 690

TABLES Table 1 – A technical comparison of current genotyping methods utilizing nextgeneration sequencing of multiplex barcoded libraries. Adapted from Wang et al. (2012). Flavors of genotyping using next generation sequencing of multiplex DNA-barcoded reduced-representation libraries Method Multiplex Shotgun Genotyping (MSG) Restriction Association DNA (RAD-seq) Double Digest RADseq (ddRADseq) 2b-RAD Genotyping-by-Sequencing (GBS) Genotyping-by-Sequencing (GBS) – two enzyme Sequence-Based Genotyping (SBG)

Random shearing No

Size selection Yes

Fragment size size selected

Enzymes 1

Yes

Yes

size selected

No

Yes

No No

Analysis tool(s)

SbfI, EcoRI

Multiplexing level 2 96 (up to 384) 96

size selected

EcoRI-MspI

48 3

MUSCLE

No No

33-36 bp < 350 bp

BsaXI 4 ApeKI 5

Custom Perl scripts TASSEL

No

No

< 350 bp

PstI-MspI

No

Yes

size selected

EcoRI-MseI PstI-TaqI

NA 48 (up to 384) 48 (up to 384) 32

MseI

1 All of these approaches can utilize different enzymes. Shown are the enzyme(s) used in the initial study. 2 All of these methods have the possibility to increase the number of multiplexed samples using more unique

barcodes. The multiplex levels was the number of samples reported in the first paper. Given in parenthesis are subsequent increases. 3 Combinatorial barcoding is possible, placing a barcode on each end of the DNA fragment. Using a set of 48 adapter P1 barcodes and x 12 PCR2 indices it is possible to uniquely label 576 individuals [48 (adapter P1 barcodes) x 12 (PCR2 indices)]. This method would require paired-end sequencing. 4 Uses type II restriction endonucleases

5 Has been successfully applied to using PstI and HindIII (Buckler et al, personal communication)

Burrows-Wheeler Alignment tool Custom Perl scripts

TASSEL Burrows-Wheeler Alignment tool; Unif Genotyper


691 692

Restriction Enzyme Sequence Comparative Analysis (RESCAN)

No

6 96-plexing reported but unpublished

Yes

size selected

MseI, NlaIII

NA 6

Burrows-Wheeler Alignment tool; Samtools



